CDC Daily by State
davegoblue
2022-09-07
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v004, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions
that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download
on the CDC
website
- CDC hospital data are available for download on the healthdata.gov
website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(geofacet)
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download
data
- renMapper - mapping file for renaming of variables in the raw data
file
- selfListMapper - mapping file for transformations by variable
type
- fullListMapper - mapping file for transformations across variable
types
- lstComboMapper - mapping file for elements to be combined by data
type (most common is to combine NYC and NYS data to NY if the file
provides them separately)
- uqMapper - mapping file for fields that should combine to be unique
keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common
is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control
total checks
- checkControlAggMapper - mapping file for numeric variables for
control total checks
- checkSimilarityMapper - mapping file for similarity checks to
perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total
checks to perform
- perCapMapper - named vector that drives conversion from original
field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example Code Processing
The function is run to download and process the latest CDC case, hospitalization, and death data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220907.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220907.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220907.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220805")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220805")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220805")$dfRaw$vax
)
cdc_daily_220907 <- readRunCDCDaily(thruLabel="Sep 05, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 57480 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-07-31 new_deaths 116 23 93 1.33812950
## 2 2022-07-30 new_deaths 130 34 96 1.17073171
## 3 2022-07-23 new_deaths 158 109 49 0.36704120
## 4 2022-07-24 new_deaths 170 126 44 0.29729730
## 5 2022-08-01 new_deaths 433 347 86 0.22051282
## 6 2022-07-28 new_deaths 518 434 84 0.17647059
## 7 2022-07-16 new_deaths 151 127 24 0.17266187
## 8 2022-07-29 new_deaths 639 543 96 0.16243655
## 9 2022-07-25 new_deaths 306 265 41 0.14360771
## 10 2022-08-03 new_deaths 716 632 84 0.12462908
## 11 2022-08-02 new_deaths 715 632 83 0.12323682
## 12 2022-07-27 new_deaths 703 634 69 0.10321616
## 13 2022-07-10 new_deaths 114 103 11 0.10138249
## 14 2022-07-22 new_deaths 628 580 48 0.07947020
## 15 2022-06-18 new_deaths 105 97 8 0.07920792
## 16 2022-07-26 new_deaths 643 596 47 0.07586764
## 17 2022-07-04 new_deaths 138 128 10 0.07518797
## 18 2022-07-18 new_deaths 361 337 24 0.06876791
## 19 2022-07-21 new_deaths 500 471 29 0.05973223
## 20 2022-07-09 new_deaths 110 104 6 0.05607477
## 21 2022-07-30 new_cases 38338 32823 5515 0.15500063
## 22 2022-07-31 new_cases 39803 35276 4527 0.12059298
## 23 2022-08-01 new_cases 126242 133013 6771 0.05223429
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC tot_deaths 11352019 11325521 26498 0.002336938
## 2 KY tot_deaths 6954858 6939424 15434 0.002221633
## 3 NC new_deaths 26101 25692 409 0.015793640
## 4 KY new_deaths 16647 16438 209 0.012634124
## 5 FL new_deaths 78609 77823 786 0.010049095
## 6 AL new_deaths 20081 19974 107 0.005342654
## 7 SC new_deaths 18211 18192 19 0.001043870
## 8 SC new_cases 1626423 1605165 21258 0.013156380
## 9 KY new_cases 1489715 1479668 10047 0.006767062
## 10 NC new_cases 3026839 3022204 4635 0.001532474
##
##
##
## Raw file for cdcDaily:
## Rows: 57,480
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-09-01, 2021-0…
## $ state <chr> "KS", "ND", "AS", "ND", "IN", "FL", "TN", "PR", "PW", "…
## $ tot_cases <dbl> 297229, 163565, 11, 118491, 668765, 3510205, 64885, 173…
## $ conf_cases <dbl> 241035, 135705, NA, 107475, NA, NA, 64371, 144788, NA, …
## $ prob_cases <dbl> 56194, 27860, NA, 11016, NA, NA, 514, 29179, NA, NA, NA…
## $ new_cases <dbl> 0, 589, 0, 536, 487, 9979, 1816, 667, 0, 317, 0, 28, 8,…
## $ pnew_case <dbl> 0, 220, 0, 66, 0, 2709, 30, 274, 0, 0, 0, 5, 0, 46, 70,…
## $ tot_deaths <dbl> 4851, 1907, 0, 1562, 12710, 56036, 749, 2911, 0, 561, 0…
## $ conf_death <dbl> NA, NA, NA, NA, 12315, NA, 722, 2482, NA, NA, NA, 1601,…
## $ prob_death <dbl> NA, NA, NA, NA, 395, NA, 27, 429, NA, NA, NA, 366, NA, …
## $ new_deaths <dbl> 0, 9, 0, 1, 7, 294, 8, 8, 0, 12, 0, 0, 0, 5, 0, 4, 0, 0…
## $ pnew_death <dbl> 0, 0, 0, 0, 2, 26, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases <chr> "Agree", "Agree", NA, "Agree", "Not agree", "Not agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Agree", "Not agre…## Rows: 49367 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-07-25 hosp_ped 3964 4594 630 0.1472307
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 ND inp 122358 122070 288 0.002356522
## 2 NH hosp_ped 1127 1167 40 0.034873583
## 3 KS hosp_ped 4891 4725 166 0.034525790
## 4 ME hosp_ped 2387 2338 49 0.020740741
## 5 KY hosp_ped 20285 20665 380 0.018559219
## 6 WV hosp_ped 5686 5753 67 0.011714311
## 7 VA hosp_ped 18388 18192 196 0.010716238
## 8 TN hosp_ped 22215 22423 208 0.009319414
## 9 NM hosp_ped 8054 8114 60 0.007422068
## 10 SC hosp_ped 9035 9092 57 0.006288961
## 11 DE hosp_ped 5277 5310 33 0.006234061
## 12 NJ hosp_ped 19499 19618 119 0.006084311
## 13 UT hosp_ped 10271 10210 61 0.005956740
## 14 MS hosp_ped 11803 11854 51 0.004311620
## 15 AL hosp_ped 20947 21025 78 0.003716764
## 16 VT hosp_ped 540 542 2 0.003696858
## 17 WY hosp_ped 859 856 3 0.003498542
## 18 MA hosp_ped 12619 12657 38 0.003006805
## 19 NC hosp_ped 30541 30453 88 0.002885530
## 20 PR hosp_ped 23021 22959 62 0.002696825
## 21 IL hosp_ped 44084 44202 118 0.002673131
## 22 AK hosp_ped 2664 2657 7 0.002631084
## 23 MO hosp_ped 39841 39939 98 0.002456756
## 24 PA hosp_ped 55078 55211 133 0.002411845
## 25 AR hosp_ped 12767 12747 20 0.001567767
## 26 CO hosp_ped 22421 22387 34 0.001517586
## 27 OH hosp_ped 91382 91261 121 0.001324989
## 28 AZ hosp_ped 27563 27532 31 0.001125329
## 29 MD hosp_ped 17240 17221 19 0.001102696
## 30 ND hosp_adult 115920 115630 290 0.002504859
##
##
##
## Raw file for cdcHosp:
## Rows: 49,367
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Rows: 36184 Columns: 96
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Distributed_Novavax Administered_Novavax Series_Complete_Novavax
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,184
## Columns: 96
## $ date <date> 2022-08-31, 2022-08-31, 2022-0…
## $ MMWR_week <dbl> 35, 35, 35, 35, 35, 35, 35, 35,…
## $ state <chr> "PW", "SD", "MA", "HI", "RI", "…
## $ Distributed <dbl> 47090, 2141765, 18793570, 38391…
## $ Distributed_Janssen <dbl> 3800, 92800, 626200, 124700, 90…
## $ Distributed_Moderna <dbl> 30000, 847500, 7168380, 1461820…
## $ Distributed_Pfizer <dbl> 13290, 1199665, 10993590, 22498…
## $ Distributed_Novavax <dbl> 0, 1800, 5400, 2800, 3200, 200,…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 218698, 242101, 272667, 271153,…
## $ Distributed_Per_100k_5Plus <dbl> 231139, 260083, 287577, 288518,…
## $ Distributed_Per_100k_12Plus <dbl> 252561, 290172, 312354, 316997,…
## $ Distributed_Per_100k_18Plus <dbl> 283966, 320836, 339252, 344011,…
## $ Distributed_Per_100k_65Plus <dbl> 2363960, 1410250, 1607210, 1430…
## $ vxa <dbl> 49416, 1511407, 15773792, 31561…
## $ Administered_5Plus <dbl> 49373, 1507671, 15687231, 31448…
## $ Administered_12Plus <dbl> 46683, 1451767, 15028043, 30184…
## $ Administered_18Plus <dbl> 43018, 1359766, 14038745, 28178…
## $ Administered_65Plus <dbl> 5346, 453691, 3769576, 842523, …
## $ Administered_Janssen <dbl> 2357, 42334, 407539, 71355, 664…
## $ Administered_Moderna <dbl> 37794, 586034, 6193704, 1157104…
## $ Administered_Pfizer <dbl> 9098, 882891, 9171774, 1927032,…
## $ Administered_Novavax <dbl> 0, 0, 295, 10, 219, 1, 45, 25, …
## $ Administered_Unk_Manuf <dbl> 167, 148, 480, 697, 2259, 9, 25…
## $ Admin_Per_100k <dbl> 229500, 170846, 228854, 222915,…
## $ Admin_Per_100k_5Plus <dbl> 242345, 183083, 240044, 236338,…
## $ Admin_Per_100k_12Plus <dbl> 250378, 196689, 249770, 249232,…
## $ Admin_Per_100k_18Plus <dbl> 259410, 203693, 253421, 252497,…
## $ Admin_Per_100k_65Plus <dbl> 268373, 298734, 322370, 313850,…
## $ Recip_Administered <dbl> 49797, 1533994, 15858274, 31873…
## $ Administered_Dose1_Recip <dbl> 20575, 700878, 6982383, 1266721…
## $ Administered_Dose1_Pop_Pct <dbl> 95.0, 79.2, 95.0, 89.5, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus <dbl> 20547, 698199, 6928829, 1259039…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 95.0, 84.8, 95.0, 94.6, 95.0, 9…
## $ Administered_Dose1_Recip_12Plus <dbl> 19119, 668658, 6593289, 1196906…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 90.6, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_18Plus <dbl> 17584, 622099, 6127404, 1107067…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 93.2, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_65Plus <dbl> 1876, 182192, 1462735, 275645, …
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.2, 95.0, 95.0, 95.0, 95.0, 8…
## $ vxc <dbl> 18338, 563276, 5570460, 1128707…
## $ vxcpoppct <dbl> 85.2, 63.7, 80.8, 79.7, 84.9, 8…
## $ Series_Complete_5Plus <dbl> 18330, 563050, 5551263, 1126786…
## $ Series_Complete_5PlusPop_Pct <dbl> 90.0, 68.4, 84.9, 84.7, 89.4, 9…
## $ Series_Complete_12Plus <dbl> 17241, 539812, 5278994, 1071938…
## $ Series_Complete_12PlusPop_Pct <dbl> 92.5, 73.1, 87.7, 88.5, 92.3, 9…
## $ vxcgte18 <dbl> 15791, 503622, 4896487, 990569,…
## $ vxcgte18pct <dbl> 95.0, 75.4, 88.4, 88.8, 93.0, 9…
## $ vxcgte65 <dbl> 1811, 151094, 1165453, 252765, …
## $ vxcgte65pct <dbl> 90.9, 95.0, 95.0, 94.2, 95.0, 8…
## $ Series_Complete_Janssen <dbl> 2361, 39918, 384642, 66056, 611…
## $ Series_Complete_Moderna <dbl> 12724, 204498, 1968460, 371324,…
## $ Series_Complete_Pfizer <dbl> 3164, 318781, 3216940, 691089, …
## $ Series_Complete_Novavax <dbl> 0, 2, 38, 1, 52, 1, 6, 6, 38, 2…
## $ Series_Complete_Unk_Manuf <dbl> 82, 70, 290, 215, 602, 3, 591, …
## $ Series_Complete_Janssen_5Plus <dbl> 2361, 39914, 384637, 66028, 611…
## $ Series_Complete_Moderna_5Plus <dbl> 12724, 204289, 1955713, 370535,…
## $ Series_Complete_Pfizer_5Plus <dbl> 3163, 318775, 3210586, 690007, …
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 82, 70, 289, 215, 587, 3, 591, …
## $ Series_Complete_Janssen_12Plus <dbl> 2361, 39912, 384612, 66026, 611…
## $ Series_Complete_Moderna_12Plus <dbl> 12724, 204257, 1953836, 370413,…
## $ Series_Complete_Pfizer_12Plus <dbl> 2074, 295572, 2940222, 635307, …
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 82, 69, 286, 191, 572, 3, 588, …
## $ Series_Complete_Janssen_18Plus <dbl> 2361, 39882, 383306, 65839, 611…
## $ Series_Complete_Moderna_18Plus <dbl> 12723, 204149, 1948279, 369555,…
## $ Series_Complete_Pfizer_18Plus <dbl> 625, 259525, 2564603, 555015, 4…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 82, 64, 262, 159, 543, 3, 574, …
## $ Series_Complete_Janssen_65Plus <dbl> 227, 5079, 74665, 11821, 6832, …
## $ Series_Complete_Moderna_65Plus <dbl> 1542, 74263, 531381, 111196, 86…
## $ Series_Complete_Pfizer_65Plus <dbl> 40, 71727, 559321, 129727, 1005…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 2, 25, 80, 21, 162, 0, 263, 69,…
## $ Additional_Doses <dbl> 12048, 248903, 2987198, 646528,…
## $ Additional_Doses_Vax_Pct <dbl> 65.7, 44.2, 53.6, 57.3, 56.1, 5…
## $ Additional_Doses_5Plus <dbl> 12048, 248900, 2987162, 646515,…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 65.7, 44.2, 53.8, 57.4, 56.2, 5…
## $ Additional_Doses_12Plus <dbl> 11872, 246180, 2938665, 637193,…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 68.9, 45.6, 55.7, 59.4, 58.4, 5…
## $ Additional_Doses_18Plus <dbl> 11181, 236970, 2791202, 606621,…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 70.8, 47.1, 57.0, 61.2, 60.1, 5…
## $ Additional_Doses_50Plus <dbl> 4815, 163923, 1630617, 376685, …
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 80.1, 58.2, 66.0, 75.0, 71.4, 7…
## $ Additional_Doses_65Plus <dbl> 1575, 98849, 840257, 208155, 15…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 87.0, 65.4, 72.1, 82.4, 79.0, 7…
## $ Additional_Doses_Moderna <dbl> 10870, 109000, 1349812, 272149,…
## $ Additional_Doses_Pfizer <dbl> 1176, 136782, 1609614, 367759, …
## $ Additional_Doses_Janssen <dbl> 2, 3093, 27721, 6500, 5296, 217…
## $ Additional_Doses_Unk_Manuf <dbl> 0, 26, 45, 118, 129, 0, 438, 78…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 1126, 53725, 590595, 170399, 10…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 23.4, 32.8, 36.2, 45.2, 34.4, 1…
## $ Second_Booster_65Plus <dbl> 383, 39382, 379347, 111314, 667…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 24.3, 39.8, 45.1, 53.5, 43.5, 2…
## $ Second_Booster_Janssen <dbl> 0, 27, 253, 120, 151, 1, 119, 2…
## $ Second_Booster_Moderna <dbl> 1148, 24919, 309097, 87335, 498…
## $ Second_Booster_Pfizer <dbl> 22, 30731, 314869, 91565, 57827…
## $ Second_Booster_Unk_Manuf <dbl> 0, 2, 10, 15, 53, 0, 80, 28, 21…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.53e+10 5.16e+8 93993694 1025546 56522
## 2 after 3.51e+10 5.14e+8 92929415 1019927 48858
## 3 pctchg 6.80e- 3 4.57e-3 0.0113 0.00548 0.136
##
##
## Processed for cdcDaily:
## Rows: 48,858
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2021-09-01, 2021-03-08, 2021-09-17…
## $ state <chr> "KS", "ND", "ND", "IN", "FL", "TN", "IA", "SD", "HI", "MA",…
## $ tot_cases <dbl> 297229, 163565, 118491, 668765, 3510205, 64885, 20015, 1226…
## $ tot_deaths <dbl> 4851, 1907, 1562, 12710, 56036, 749, 561, 1967, 17, 17818, …
## $ new_cases <dbl> 0, 589, 536, 487, 9979, 1816, 317, 28, 8, 451, 1040, 133, 0…
## $ new_deaths <dbl> 0, 9, 1, 7, 294, 8, 12, 0, 0, 5, 4, 0, 0, 5, 1, 3, 0, 0, 22…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.11e+7 4.45e+7 1229807 49367
## 2 after 5.09e+7 4.43e+7 1205197 47181
## 3 pctchg 5.37e-3 5.13e-3 0.0200 0.0443
##
##
## Processed for cdcHosp:
## Rows: 47,181
## Columns: 5
## $ date <date> 2021-01-06, 2021-01-06, 2020-12-31, 2020-12-30, 2020-12-29…
## $ state <chr> "MA", "OR", "SD", "RI", "OR", "OH", "LA", "WV", "VT", "WY",…
## $ inp <dbl> 2232, 583, 282, 471, 626, 5534, 1461, 242, 0, 71, 1, 91, 49…
## $ hosp_adult <dbl> 2209, 568, 280, 469, 615, 5443, 1449, 241, 0, 70, 1, 90, 48…
## $ hosp_ped <dbl> 23, 15, 2, 2, 11, 91, 12, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.14e+11 1.70e+11 1511640. 4.31e+10 2.24e+6 1.57e+11 1.78e+6 3.62e+4
## 2 after 2.00e+11 8.22e+10 1265373. 2.09e+10 1.98e+6 7.61e+10 1.51e+6 2.86e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹​vxcgte65pct, ²​vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,611
## Columns: 9
## $ date <date> 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-3…
## $ state <chr> "SD", "MA", "HI", "RI", "MT", "WY", "LA", "KS", "IN", "MS"…
## $ vxa <dbl> 1511407, 15773792, 3156198, 2353711, 1675440, 778457, 6536…
## $ vxc <dbl> 563276, 5570460, 1128707, 899544, 618143, 300240, 2526855,…
## $ vxcpoppct <dbl> 63.7, 80.8, 79.7, 84.9, 57.8, 51.9, 54.4, 63.3, 56.8, 53.0…
## $ vxcgte65 <dbl> 151094, 1165453, 252765, 194268, 180238, 84688, 642150, 44…
## $ vxcgte65pct <dbl> 95.0, 95.0, 94.2, 95.0, 87.3, 85.4, 86.7, 94.6, 88.5, 85.2…
## $ vxcgte18 <dbl> 503622, 4896487, 990569, 794734, 562722, 275699, 2323648, …
## $ vxcgte18pct <dbl> 75.4, 88.4, 88.8, 93.0, 67.0, 62.0, 65.2, 74.2, 67.0, 63.3…
##
## Integrated per capita data file:
## Rows: 49,071
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_220907, ovrWriteError=FALSE)The function is run to download and process the latest hospitalization data:
# Run for latest data, save as RDS
indivHosp_20220907 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 269456 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 269,456
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 5077
## 2 Critical Access Hospitals 72331
## 3 Long Term 18519
## 4 Short Term 173529
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 54
## 2 GU 106
## 3 MP 46
## 4 PR 2864
## 5 VI 106
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 64869 204079 508 269456
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 143 247227 22086 269456
## 3 icu_beds_used_7_day_avg 64 237310 32082 269456
## 4 inpatient_beds_7_day_avg 67 268366 1023 269456
## 5 inpatient_beds_used_7_day_avg 51 248042 21363 269456
## 6 inpatient_beds_used_covid_7_day_avg 32 182000 87424 269456
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 162 184312 84982 269456
## 8 total_adult_patients_hospitalized_confirmed_and… 121 181944 87391 269456
## 9 total_beds_7_day_avg 63149 206009 298 269456
## 10 total_icu_beds_7_day_avg 74 255402 13980 269456
## # … with abbreviated variable names ¹​Positive, ²​`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220907, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220907.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLPost-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220907 <- postProcessCDCDaily(cdc_daily_220907,
dataThruLabel="Aug 2022",
keyDatesBurden=c("2022-08-31", "2022-02-28",
"2021-08-31", "2021-02-28"
),
keyDatesVaccine=c("2022-08-31", "2022-03-31",
"2021-10-31", "2021-05-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220907 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220907,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220907$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.70e4 49367
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.86e5 49367
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.12e5 49367
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 4.96e5 49367
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 7.91e5 49367
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.04e6 49367
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.04e6 49367
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.32e5 49367
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.83e4 49367
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.56e5 49367
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.35e5 49367
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.39e5 49367
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.37e5 49367
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.38e5 49367
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.19e5 49367
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.55e5 49367
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.67e5 49367
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.74e5 49367saveToRDS(burdenPivotList_220907, ovrWriteError=FALSE)
saveToRDS(hospPerCap_220907, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220907)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 7,740 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 7,730 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity is updated using a mix of old data (for 2021) and new data:
identical(names(indivHosp_20220907), names(readFromRDS("indivHosp_20220704")))## [1] TRUEmodHospData <- bind_rows(filter(readFromRDS("indivHosp_20220704"), lubridate::year(collection_week)<2022),
filter(indivHosp_20220907, lubridate::year(collection_week)>=2022),
.id="src"
)
updated_modStateHosp_20220907 <- hospitalCapacityCDCDaily(modHospData,
plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
)
Data availability by source and time is assessed:
# Temporary function to aggregate data
tempCounter <- function(df) {
df %>%
select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
colRenamer(vecRename=hhsMapper) %>%
pivot_longer(-c(hospital_pk, collection_week)) %>%
filter(!is.na(value), value>0) %>%
count(collection_week, name)
}
dfTemp <- bind_rows(tempCounter(indivHosp_20220907), tempCounter(readFromRDS("indivHosp_20220704")), .id="src")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Number of hospitals in US reporting >0 on metric by week", x=NULL, y="# Hospitals Reporting > 0") +
scale_color_discrete("Data Source:")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
group_by(collection_week, name) %>%
summarize(delta=sum(ifelse(src=="SEP-2022", n, 0)-ifelse(src!="SEP-2022", n, 0)), .groups="drop") %>%
ggplot(aes(x=collection_week, y=delta)) +
geom_line(aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))) +
geom_hline(yintercept=0, lty=2) +
geom_vline(xintercept=c(as.Date("2021-08-20"), as.Date("2022-06-24")), lty=2) +
scale_color_identity(NULL) +
facet_wrap(~name) +
labs(title="Delta in Number of hospitals in US reporting >0 on metric by week",
subtitle="Trend break dashed lines at 2021-08-20 and 2022-06-24",
x=NULL,
y="Delta in # Hospitals Reporting > 0"
)